History of Election in India

The Indian general election of 1951–52, held from 25 October 1951 to 21 February 1952, was the first election to the Lok Sabha since India became independent in August 1947.[1][2][3] It was conducted under the provisions of the Indian Constitution, which was adopted on 26 November 1949. Elections to most of the state legislatures took place simultaneously.

Political parties

A total of 53 parties and 533 independents contested the 489 seats in the election.[12] Two former cabinet colleagues of Nehru established separate political parties to challenge the INC's supremacy. While Syama Prasad Mukherjee went on to found the Jana Sangh in October 1951, first Law Minister B. R. Ambedkar revived the Scheduled Castes Federation (which was later named the Republican Party).

Other parties which started coming to the forefront included the Kisan Mazdoor Praja Parishad, whose prime mover was Acharya Kripalani; the Socialist Party, which had Ram Manohar Lohia and Jayaprakash Narayan's leadership to boast of; and the Communist Party of India. However, these smaller parties were unable to make an electoral stand against the Indian National Congress.

The image showing the result of first election result

---

Bengal contributed 42 seats out of 543 seats for gernal election

Image is showing the 2019 gernal election result in Bengal where

1. 22 Seat- AITC
2. 18 Seat- BJP
3. 2 Seat- INC

Bengal Election

Importing the libraries

import numpy as np 
import pandas as pd
import pandas_profiling
import scipy as sc
import sklearn 
from sklearn import preprocessing
from sklearn.metrics import accuracy_score,mean_squared_error
from sklearn.preprocessing import MinMaxScaler
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import math
from sklearn.model_selection import KFold
from sklearn.model_selection import GridSearchCV
import seaborn as sns 
from sklearn.ensemble import RandomForestClassifier
import urllib.request
from pprint import pprint
import pandas_profiling 
import shapash
from html_table_parser import HTMLTableParser
import warnings 

warnings.filterwarnings("ignore")

Scrapping the data from wikipedia for the population distribution in west bengal

Source of the data is Wikipedia

class web_scrap:
    def __init__(self,url,number):
        self.url = url
        self.number = number
    def Table_scrap(self):
        request = urllib.request.Request(self.url)
        open_link = urllib.request.urlopen(request)
        xhtml = open_link.read().decode("utf-8")
        table_content = HTMLTableParser()
        table_content.feed(xhtml)
        return table_content.tables[self.number]

Table_of_each_population= web_scrap("https://en.wikipedia.org/wiki/West_Bengal",3).Table_scrap()

Bengal_population_Dataframe = pd.DataFrame(Table_of_each_population[1:len(Table_of_each_population)+1],columns = Table_of_each_population[0])

Data Description

1. District : District of Bengal
2. Population : Population of that District
3. Growth rate : Growth rate of the district
4. Sex ratio : Sex ratio of that District
5. Literacy: Literacy of that district
6. Density per square killometer : Density of population per killometer

Bengal_population_Dataframe

	District	Population	Growth rate	Sex ratio	Literacy	Density per square Kilometer
0	North 24 Parganas	10,009,781	12.04	955	84.06	2445
1	South 24 Parganas	8,161,961	18.17	956	77.51	819
2	Purba Bardhaman	4,835,432	–	945	74.73	890
3	Paschim Bardhaman	2,882,031	–	922	78.75	1800
4	Murshidabad	7,103,807	21.09	958	66.59	1334
5	West Midnapore	5,913,457	13.86	966	78.00	631
6	Hooghly	5,519,145	9.46	961	81.80	1753
7	Nadia	5,167,600	12.22	947	74.97	1316
8	East Midnapore	5,095,875	15.36	938	87.02	1081
9	Howrah	4,850,029	13.50	939	83.31	3306
10	Kolkata	4,496,694	−1.67	908	86.31	24306
11	Maldah	3,988,845	21.22	944	61.73	1069
12	Jalpaiguri	3,872,846	13.87	953	73.25	622
13	Alipurduar [a]	1,700,000	–	–	–	400
14	Bankura	3,596,292	12.64	954	70.95	523
15	Birbhum	3,502,404	16.15	956	70.68	771
16	North Dinajpur	3,007,134	23.15	939	59.07	958
17	Purulia	2,930,115	15.52	957	64.48	468
18	Cooch Behar	2,819,086	13.71	942	74.78	832
19	Darjeeling	1,846,823	14.77	970	79.56	586
20	Dakshin Dinajpur	1,676,276	11.52	956	72.82	755
21	Kalimpong [a]	202,239	–	–	–	270
22	Jhargram [a]	1,136,548	–	–	–	374

## converting all columns into suitable data type
def variable_covertor(dataframe):
    pop = []
    for line in dataframe:
        line = line.replace(",","")
        pop.append((int(line)))
    return pop

def convertor(dataframe):
    lit = []
    for i in dataframe:
        i = i.replace("–","0.00")
        lit.append(float(i))
    return lit
    

Bengal_population_Dataframe["Literacy"] = convertor(Bengal_population_Dataframe["Literacy"])
Bengal_population_Dataframe["Sex ratio"] = convertor(Bengal_population_Dataframe["Sex ratio"])

Bengal_population_Dataframe['Population'] = variable_covertor(Bengal_population_Dataframe['Population'])

Bengal_population_Dataframe.profile_report()

visualization

class visualization:
    def __init__(self,dataframe,xvalue,yvalue,xlabel,ylabel,title):
        self.dataframe = dataframe
        self.xvalue = xvalue
        self.yvalue  = yvalue
        self.xlabel = xlabel
        self.ylabel = ylabel
        self.title = title
        
    """Creating the different visual charts for understanding"""
    def boxplot_plot(self):
        sns.boxplot(self.dataframe)
        plt.xlabel(self.xlabel);
        plt.ylabel(self.ylabel);
        plt.title(self.title);
        
      
    def countplot_without_percentage_representation(self):
        sns.set_style("darkgrid")
        sns.countplot(x=self.xvalue,data=self.dataframe)
        plt.xlabel(self.xlabel);
        plt.xticks(rotation=60)
        plt.ylabel(self.ylabel);
        plt.title(self.title);
        plt.show()
        
        
        
    def countplot_with_percentage_representation(self):
        sns.set_style("darkgrid")
        ax = sns.countplot(x=self.xvalue,data=self.dataframe)
        total = len(self.dataframe)
        for p in ax.patches:
            percentage = '{:.1f}%'.format(100 * p.get_height()/total)
            x = p.get_x() + p.get_width()
            y = p.get_height()
            ax.annotate(percentage, (x, y),ha='right')
        plt.xlabel(self.xlabel);
        plt.ylabel(self.ylabel);
        plt.xticks(rotation=60)
        plt.title(self.title);
        plt.show()
    
    
    
        
    """Scatter plot to see the multivariate outlier"""
    def scatterplot(self):
        sns.scatterplot(x=self.xvalue,y=self.yvalue,data=self.dataframe)
        plt.xlabel(self.xlabel);
        plt.title(self.title);
        plt.show()
        
        
    def heatmap(self):
        correlation_data = self.dataframe.corr()
        sns.heatmap(correlation_data,annot=True)
        plt.xlabel(self.xlabel);
        plt.title(self.title);
        plt.show()
        
    def violinplot(self):
        sns.violinplot(data=self.dataframe,x=self.xvalue,y=self.yvalue,split=True,inner="quart",linewidth=1,palette={'Yes':"b","No":".85"})
        sns.despine(left=True)
        plt.xlabel(self.xlabel);
        plt.title(self.title);
        plt.show()
        
    """Specific pie chart for weekly engagement of mundra port"""   
    def piechart(self):
        fig, ax = plt.subplots()
        District = self.dataframe[self.xvalue]
        df_pie = pd.DataFrame(District)
        df_pie["count"] = self.dataframe[self.yvalue]
        
        labels = [i for i in df_pie[self.xvalue].unique()]
        
        points = [j for j in df_pie["count"]]
    
        add_points = df_pie["count"].sum()
        add_points = int(add_points)
        per = []
        for j in  points:
            per.append(j/add_points)
        percentages = [i for i in df_pie["count"].unique()]
        explode=per
        ax.pie(percentages, explode=explode, labels=labels, autopct='%1.0f%%', shadow=False, startangle=0,pctdistance=1.2,labeldistance=1.5);
        ax.axis('equal')
        ax.set_title(self.title)
        ax.legend(frameon=False, bbox_to_anchor=(1.8,1.0));
        
        
    def bar_chart(self):
        objects = tuple(self.xvalue)
        y_pos = np.arange(len(objects))
        performance = list(self.yvalue)
        plt.bar(y_pos, performance, align='center', alpha=0.5)
        plt.xticks(y_pos, objects,rotation=60)
        plt.ylabel(self.ylabel)
        plt.title(self.title)
        plt.show()

        
    

Westbengal population distribution

visualization(Bengal_population_Dataframe,Bengal_population_Dataframe.iloc[:11,0],Bengal_population_Dataframe.iloc[:11,1],"","Population in Millions","District wise population").bar_chart()

West bengal 2016 result analysis

Bengal_2016_result_analysis = web_scrap("https://en.wikipedia.org/wiki/2016_West_Bengal_Legislative_Assembly_election",11).Table_scrap()

Bengal_2016_result_analysis_df = pd.DataFrame(Bengal_2016_result_analysis[1:len(Bengal_2016_result_analysis)+1],columns = Bengal_2016_result_analysis[0])

Bengal_2016_result_analysis_df

	District	Total	AITC	LF	UPA	NDA	Others
0	Cooch Behar	9	8	1	0	0	0
1	Jalpaiguri	7	6	0	1	0	0
2	Alipurduar	5	4	0	0	1	0
3	Darjeeling	6	0	1	2	3	0
4	North Dinajpur	9	4	2	3	0	0
5	South Dinajpur	6	2	3	1	0	0
6	Malda	12	1	1	8	1	1
7	Murshidabad	22	4	4	14	0	0
8	Nadia	17	13	1	3	0	0
9	North 24 Parganas	33	27	3	3	0	0
10	South 24 Parganas	31	29	2	0	0	0
11	Kolkata	11	11	0	0	0	0
12	Howrah	16	15	0	1	0	0
13	Hooghly	18	16	1	1	0	0
14	East Midnapore	16	13	3	0	0	0
15	West Midnapore	19	17	0	1	1	0
16	Purulia	9	7	0	2	0	0
17	Bankura	12	7	3	2	0	0
18	Burdwan	25	19	5	1	0	0
19	Birbhum	11	9	1	1	0	0
20	Total	294	211	32	44	6	1

Bengal_2016_result_analysis_df["Total"] = convertor(Bengal_2016_result_analysis_df["Total"])
Bengal_2016_result_analysis_df["AITC"] = convertor(Bengal_2016_result_analysis_df["AITC"])
Bengal_2016_result_analysis_df["LF"] = convertor(Bengal_2016_result_analysis_df["LF"])
Bengal_2016_result_analysis_df["UPA"] = convertor(Bengal_2016_result_analysis_df["UPA"])
Bengal_2016_result_analysis_df["NDA"] = convertor(Bengal_2016_result_analysis_df["NDA"])
Bengal_2016_result_analysis_df["Others"] = convertor(Bengal_2016_result_analysis_df["Others"])

Bengal_2016_result_analysis_df

	District	Total	AITC	LF	UPA	NDA	Others
0	Cooch Behar	9.0	8.0	1.0	0.0	0.0	0.0
1	Jalpaiguri	7.0	6.0	0.0	1.0	0.0	0.0
2	Alipurduar	5.0	4.0	0.0	0.0	1.0	0.0
3	Darjeeling	6.0	0.0	1.0	2.0	3.0	0.0
4	North Dinajpur	9.0	4.0	2.0	3.0	0.0	0.0
5	South Dinajpur	6.0	2.0	3.0	1.0	0.0	0.0
6	Malda	12.0	1.0	1.0	8.0	1.0	1.0
7	Murshidabad	22.0	4.0	4.0	14.0	0.0	0.0
8	Nadia	17.0	13.0	1.0	3.0	0.0	0.0
9	North 24 Parganas	33.0	27.0	3.0	3.0	0.0	0.0
10	South 24 Parganas	31.0	29.0	2.0	0.0	0.0	0.0
11	Kolkata	11.0	11.0	0.0	0.0	0.0	0.0
12	Howrah	16.0	15.0	0.0	1.0	0.0	0.0
13	Hooghly	18.0	16.0	1.0	1.0	0.0	0.0
14	East Midnapore	16.0	13.0	3.0	0.0	0.0	0.0
15	West Midnapore	19.0	17.0	0.0	1.0	1.0	0.0
16	Purulia	9.0	7.0	0.0	2.0	0.0	0.0
17	Bankura	12.0	7.0	3.0	2.0	0.0	0.0
18	Burdwan	25.0	19.0	5.0	1.0	0.0	0.0
19	Birbhum	11.0	9.0	1.0	1.0	0.0	0.0
20	Total	294.0	211.0	32.0	44.0	6.0	1.0

visualization(Bengal_2016_result_analysis_df,Bengal_2016_result_analysis_df.iloc[:20,0],Bengal_2016_result_analysis_df.iloc[:20,1],"","Number of seat be parties","Total seat district wise").bar_chart()

Performance by AITC in 2016 (All India Trinamool Congress)

visualization(Bengal_2016_result_analysis_df,Bengal_2016_result_analysis_df.iloc[:20,0],Bengal_2016_result_analysis_df.iloc[:20,2],"","Number of seat","AITC performance in 2016").bar_chart()

Performance by Left in 2016

visualization(Bengal_2016_result_analysis_df,Bengal_2016_result_analysis_df.iloc[:20,0],Bengal_2016_result_analysis_df.iloc[:20,3],"","Number of seat","Left performance in 2016").bar_chart()

Performance by UPA in 2016

visualization(Bengal_2016_result_analysis_df,Bengal_2016_result_analysis_df.iloc[:20,0],Bengal_2016_result_analysis_df.iloc[:20,4],"","Number of seat","UPA performance in 2016").bar_chart()

Performance by NDA in 2016

visualization(Bengal_2016_result_analysis_df,Bengal_2016_result_analysis_df.iloc[:20,0],Bengal_2016_result_analysis_df.iloc[:20,5],"","Number of seat","NDA performance in 2016").bar_chart()

Trinmool congress did well in 2016 election

---

Deep down analysis in 2016 election

Bengal_detail_result_analysis = pd.read_excel("Detailed Results.xlsx",header=2)

Bengal_detail_result_analysis

	Constituency No.	Constituency Name	Candidate Name	Candidate Sex	Candidate Age	Candidate Category	Party Name	VALID VOTES POLLED in General	VALID VOTES POLLED in Postal	Total Valid Votes	Total Electors	Total Votes
0	1	Mekliganj	ARGHYA ROY PRADHAN (BILU)	M	44.0	SC	AITC	74608	215	74823	202689	180960
1	1	Mekliganj	PARESH CHANDRA ADHIKARY	M	63.0	SC	AIFB	67821	365	68186	202689	180960
2	1	Mekliganj	DADHIRAM RAY	M	32.0	SC	BJP	23313	42	23355	202689	180960
3	1	Mekliganj	JYOTISH ROY	M	66.0	SC	BSP	5650	0	5650	202689	180960
4	1	Mekliganj	None of the Above	NaN	NaN	NaN	NOTA	2331	2	2333	202689	180960
...	...	...	...	...	...	...	...	...	...	...	...	...
2250	294	Murarai	HAYATUNNINSH BIBI	F	26.0	GEN	BJP	5341	4	5345	234055	199235
2251	294	Murarai	None of the Above	NaN	NaN	NaN	NOTA	1687	2	1689	234055	199235
2252	294	Murarai	BELAL SEKH	M	28.0	GEN	BSP	1376	0	1376	234055	199235
2253	294	Murarai	MANGAL HEMRAM	M	65.0	ST	SUCI	910	7	917	234055	199235
2254	294	Murarai	SAMSHUL MIYA	M	48.0	GEN	RLD	866	0	866	234055	199235

2255 rows × 12 columns

Candidates categories

Bengal_detail_result_analysis["Candidate Category"].value_counts()

GEN    1196
SC      614
ST      151
Name: Candidate Category, dtype: int64

visualization(Bengal_detail_result_analysis,"Candidate Category"," ","Count","Number of Record","Category wise candidate").countplot_with_percentage_representation()

Total 53% of gernal category candidate was there in 2016 election

---

Gender wise analysis

Bengal_detail_result_analysis["Candidate Sex"].value_counts()

M    1761
F     200
Name: Candidate Sex, dtype: int64

visualization(Bengal_detail_result_analysis,"Candidate Sex"," ","Count","Number of Record","Male vs Female candidates").countplot_with_percentage_representation()

78.1% candidates are Male and 8.9% of the candidate are female and remaining 13% records are missing but we can clearly see male candidates are in huge majority

Bengal_detail_result_analysis.columns

Index(['Constituency No.', 'Constituency Name', 'Candidate Name',
       'Candidate Sex', 'Candidate Age', 'Candidate Category', ' Party Name',
       ' VALID VOTES POLLED in General', ' VALID VOTES POLLED in Postal',
       ' Total Valid Votes', 'Total Electors', 'Total Votes'],
      dtype='object')

Bengal_detail_result_analysis[' Party Name'].unique()

array(['AITC', 'AIFB', 'BJP', 'BSP', 'NOTA', 'SUCI', 'AMB', 'IND', 'KPPU',
       'CPM', 'INC', 'WPOI', 'RSP', 'JMM', 'CPI(ML)(L)', 'ABGL', 'GOJAM',
       'GRAC', 'ABHM', 'BMUP', 'JDP', 'JD(U)', 'SP', 'SWJP', 'CPI',
       'CPIM', 'HKRD', 'SDPI', 'RPI(A)', 'SHS', 'JESM', 'PDS', 'IUML',
       'MPOI', 'LJP', 'GMM', 'NDPOI', 'TRMRPPI', 'AAAP', 'RAJSP', 'IUC',
       'RLD', 'RJD', 'JD(S)', 'MHB', 'STPI', 'FDLP', 'NCP', 'BHNP',
       'DSP(P)', 'AJSUP', 'JKP(N)', 'AKBJHP', 'JHAP', 'SP(I)', 'RCPI(R)'],
      dtype=object)

West bengal has total 294 constituency and their valid vote conut for all the candidate

Bengal_detail_result_analysis[['Constituency Name',' Total Valid Votes']].groupby('Constituency Name').plot.bar()

Constituency Name
Alipurduars         AxesSubplot(0.125,0.125;0.775x0.755)
Amdanga             AxesSubplot(0.125,0.125;0.775x0.755)
Amta                AxesSubplot(0.125,0.125;0.775x0.755)
Arambagh            AxesSubplot(0.125,0.125;0.775x0.755)
Asansol Dakshin     AxesSubplot(0.125,0.125;0.775x0.755)
                                    ...                 
Udaynarayanpur      AxesSubplot(0.125,0.125;0.775x0.755)
Uluberia Dakshin    AxesSubplot(0.125,0.125;0.775x0.755)
Uluberia Purba      AxesSubplot(0.125,0.125;0.775x0.755)
Uluberia Uttar      AxesSubplot(0.125,0.125;0.775x0.755)
Uttarpara           AxesSubplot(0.125,0.125;0.775x0.755)
Length: 293, dtype: object

Bengal_detail_result_analysis

	Constituency No.	Constituency Name	Candidate Name	Candidate Sex	Candidate Age	Candidate Category	Party Name	VALID VOTES POLLED in General	VALID VOTES POLLED in Postal	Total Valid Votes	Total Electors	Total Votes
0	1	Mekliganj	ARGHYA ROY PRADHAN (BILU)	M	44.0	SC	AITC	74608	215	74823	202689	180960
1	1	Mekliganj	PARESH CHANDRA ADHIKARY	M	63.0	SC	AIFB	67821	365	68186	202689	180960
2	1	Mekliganj	DADHIRAM RAY	M	32.0	SC	BJP	23313	42	23355	202689	180960
3	1	Mekliganj	JYOTISH ROY	M	66.0	SC	BSP	5650	0	5650	202689	180960
4	1	Mekliganj	None of the Above	NaN	NaN	NaN	NOTA	2331	2	2333	202689	180960
...	...	...	...	...	...	...	...	...	...	...	...	...
2250	294	Murarai	HAYATUNNINSH BIBI	F	26.0	GEN	BJP	5341	4	5345	234055	199235
2251	294	Murarai	None of the Above	NaN	NaN	NaN	NOTA	1687	2	1689	234055	199235
2252	294	Murarai	BELAL SEKH	M	28.0	GEN	BSP	1376	0	1376	234055	199235
2253	294	Murarai	MANGAL HEMRAM	M	65.0	ST	SUCI	910	7	917	234055	199235
2254	294	Murarai	SAMSHUL MIYA	M	48.0	GEN	RLD	866	0	866	234055	199235

2255 rows × 12 columns

Bengal_detail_result_analysis.columns

Index(['Constituency No.', 'Constituency Name', 'Candidate Name',
       'Candidate Sex', 'Candidate Age', 'Candidate Category', ' Party Name',
       ' VALID VOTES POLLED in General', ' VALID VOTES POLLED in Postal',
       ' Total Valid Votes', 'Total Electors', 'Total Votes'],
      dtype='object')

visualization(Bengal_detail_result_analysis['Candidate Age'],"","","Candidates age","","Candidates age anaysis").boxplot_plot()

• 25% or first quartile is approx 40 years
• Median age is 48 years
• IQR is for 40-59 years

Performance_of_political_parties_2016 = pd.read_excel("Performance of Poltical Parties.xlsx",header = 1)

Performance_of_political_parties_2016.head(5)

	Party Type	Party Name	Contested	Won	Forfitted	Votes	Total Valid Votes	Votes in %	Total Valid Votes in Seat Contested	VOTE % IN SEATS CONTESTED
0	N	Bharatiya Janata Party	291	3	263	5555134	54697791	10.156048	54236459	10.242435
1	N	Bahujan Samaj Party	161	0	161	300294	54697791	0.549006	29810643	1.007338
2	N	Communist Party of India	11	1	0	791925	54697791	1.447819	2124882	37.269128
3	N	Communist Party of India (Marxist)	148	26	0	10802058	54697791	19.748618	28059913	38.496406
4	N	Indian National Congress	92	44	4	6700938	54697791	12.250838	16660837	40.219696

def piechart_party(dataframe):
        fig, ax = plt.subplots()
        seat_won_by_parties = ["All India Trinamool Congress","Indian National Congress","Communist Party of India (Marxist)","Bharatiya Janata Party"]
        df_pie = pd.DataFrame(seat_won_by_parties,columns =["seat_won_by_parties"] )
        seat_won = dataframe["Won"].sort_values(ascending= False)
        seat_won_by_parties_top4 = seat_won[:4]
        seat = seat_won_by_parties_top4
        
        df_pie["count"] = list(seat)
        
        labels = [i for i in df_pie["seat_won_by_parties"]]
        
        points = [j for j in df_pie["count"]]
        add_points = df_pie["count"].sum()
        per = []
        for j in  points:
            per.append(j/add_points)
        percentages = [i for i in df_pie["count"]]
        explode=per
        ax.pie(percentages, explode=explode, labels=labels, autopct='%1.0f%%', shadow=False, startangle=0,pctdistance=1.2,labeldistance=1.32);
        ax.axis('equal')
        ax.set_title("Parties final seat won 2016")
        ax.legend(frameon=False, bbox_to_anchor=(1.5,1.0));
    

piechart_party(Performance_of_political_parties_2016)

---

Lets understand the twitter current anaysis and understand the current situation and anlysis

These are 100 randon tweets

Bengal_2021_twitter_analysis = pd.read_csv("Bengal_sentiment.csv",encoding = "ISO-8859-1")
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)

As per twitter trending analysis and several other media platform BJP and AITC are the two major parties this year in the election

Bengal_2021_twitter_analysis.head()

	Statment	Review BJP	Review AITC
0	Over 125 BJP workers have been massacred in Be...	positive	negative
1	West Bengal has long suffered due to @mamataof...	positive	negative
2	EVMs and VVPATs found at the residence of a TM...	positive	negative
3	Amit Shah said if BJP doesn't win in Bengal, i...	negative	positive
4	TMC candidate Manoranjan Byapari, a former ref...	negative	positive

Bengal_2021_twitter_analysis.columns = ["Tweets","Review BJP","Review AITC"]

Bengal_2021_twitter_analysis

	Tweets	Review BJP	Review AITC
0	Over 125 BJP workers have been massacred in Be...	positive	negative
1	West Bengal has long suffered due to @mamataof...	positive	negative
2	EVMs and VVPATs found at the residence of a TM...	positive	negative
3	Amit Shah said if BJP doesn't win in Bengal, i...	negative	positive
4	TMC candidate Manoranjan Byapari, a former ref...	negative	positive
...	...	...	...
94	Today's hired high-tech election strategists a...	positive	negative
95	National media's obsession to term anything an...	negative	positive
96	Today election is tailor only when they will t...	positive	negative
97	So many powerful people from Hyderabad, up, bi...	negative	positive
98	Election battle in #WestBengalAssemblyElection...	NaN	NaN

99 rows × 3 columns

Bengal_2021_twitter_analysis["Review BJP"].value_counts()

positive     54
negative     39
Name: Review BJP, dtype: int64

visualization(Bengal_2021_twitter_analysis,"Review BJP","","Count","Number of Record","Tweets in favour Vs Tweets Aganist").countplot_with_percentage_representation()

54.5% tweets are in favour for BJP

from PIL import Image as PILImage
import base64, io, IPython
def image_import(image):
    image = PILImage.open(image)
    output = io.BytesIO()
    image.save(output, format='PNG')
    encoded_string = base64.b64encode(output.getvalue()).decode()
    html = '<img src="data:image/png;base64,{}"/>'.format(encoded_string)
    return IPython.display.HTML(html)

image_import("Modi_ji.png")

BJP may be leading in the Social media sites but it is just small random sample and we need to understand more about it

---

Lets understand more about these tweets

Bengal_2021_twitter_analysis['Tweets']=[word.lower() for word in Bengal_2021_twitter_analysis['Tweets']]

Reviews_list = [word.lower() for word in Bengal_2021_twitter_analysis['Tweets']]

Reviews_list[0]

'over 125 bjp workers have been massacred in bengal at the instigation of @mamataofficial in order to further her political motives in west bengal and to make space for her nephew. is didi going to run away from this reality and hide behind closed doors as always?'

import nltk
nltk.download('punkt')
nltk.download('averaged_perceptron_tagger')

[nltk_data] Downloading package punkt to
[nltk_data]     C:\Users\mayan\AppData\Roaming\nltk_data...
[nltk_data]   Package punkt is already up-to-date!
[nltk_data] Downloading package averaged_perceptron_tagger to
[nltk_data]     C:\Users\mayan\AppData\Roaming\nltk_data...
[nltk_data]   Package averaged_perceptron_tagger is already up-to-
[nltk_data]       date!

True

# Fist I tokkenize in sent tokken
Sentence_tokkenize = [nltk.sent_tokenize(sentence) for sentence in Reviews_list]

words_tokkenize =[nltk.word_tokenize(words) for words in  Reviews_list]

from nltk.corpus import stopwords 
nltk.download("stopwords")
stopword_all = stopwords.words('english')

[nltk_data] Downloading package stopwords to
[nltk_data]     C:\Users\mayan\AppData\Roaming\nltk_data...
[nltk_data]   Package stopwords is already up-to-date!

Tagged_words = [nltk.pos_tag(words_pos) for words_pos in words_tokkenize ]

  # Noun tagging
def noun_tagging(lst):
    Noun_tag_pos =[]
    for j in range(len(lst)):
        for noun_tg in lst[j]:
            if noun_tg[1]=="NN" or noun_tg[1]=="NNP" or noun_tg[1]=="NNPS" or noun_tg[1]=="NNS":
                Noun_tag_pos.append(noun_tg)
    return Noun_tag_pos

noun_tagging(Tagged_words[:1])

[('workers', 'NNS'),
 ('bengal', 'NN'),
 ('instigation', 'NN'),
 ('mamataofficial', 'NN'),
 ('order', 'NN'),
 ('motives', 'NNS'),
 ('bengal', 'NN'),
 ('space', 'NN'),
 ('nephew', 'NN'),
 ('reality', 'NN'),
 ('hide', 'NN'),
 ('doors', 'NNS')]

from nltk.stem import WordNetLemmatizer
lemmatizer = WordNetLemmatizer()
from nltk.tokenize import RegexpTokenizer
Rg_tokenizer = RegexpTokenizer(r'\w+')
import re
import nltk
nltk.download('stopwords')
from nltk.corpus import stopwords
nltk.download('wordnet')

corpus = []
for i in range(0, len(Tagged_words)):
    review = re.sub('[^a-zA-Z]', ' ', Bengal_2021_twitter_analysis['Tweets'][i])
    review = review.lower()
    review = review.split()
    all_stopwords = stopwords.words('english')
    review = [lemmatizer.lemmatize(word,'v') for word in review if not word in set(all_stopwords)]
    review = ' '.join(review)
    corpus.append(review)

[nltk_data] Downloading package stopwords to
[nltk_data]     C:\Users\mayan\AppData\Roaming\nltk_data...
[nltk_data]   Package stopwords is already up-to-date!
[nltk_data] Downloading package wordnet to
[nltk_data]     C:\Users\mayan\AppData\Roaming\nltk_data...
[nltk_data]   Package wordnet is already up-to-date!

# Removing all punctuations
import string
corpus_upd= [''.join(c for c in s if c not in string.punctuation) for s in corpus]
word_tokken_1=[nltk.word_tokenize(e) for e in corpus_upd]

import gensim
from gensim.utils import simple_preprocess
processed_docs = []

for doc in word_tokken_1:
    processed_docs.append((doc))

print(processed_docs[:2])

[['bjp', 'workers', 'massacre', 'bengal', 'instigation', 'mamataofficial', 'order', 'political', 'motives', 'west', 'bengal', 'make', 'space', 'nephew', 'didi', 'go', 'run', 'away', 'reality', 'hide', 'behind', 'close', 'doors', 'always'], ['west', 'bengal', 'long', 'suffer', 'due', 'mamataofficial', 'thirst', 'power', 'power', 'hungry', 'cm', 'sacrifice', 'need', 'people', 'order', 'restrict', 'central', 'government', 'good', 'scheme', 'implement', 'west', 'bengal', 'bengal', 'reject', 'didi']]

dictionary = gensim.corpora.Dictionary(processed_docs)

dictionary.filter_extremes(no_below=1, no_above=0.1, keep_n= 100)

bow_corpus = [dictionary.doc2bow(doc) for doc in processed_docs]

bow_corpus[21]

[(29, 1), (40, 1), (67, 1), (70, 1), (71, 1)]

document_num = 1
bow_doc_x = bow_corpus[document_num]

for i in range(len(bow_doc_x)):
    print("Word {} (\"{}\") appears {} time.".format(bow_doc_x[i][0], 
                                                     dictionary[bow_doc_x[i][0]], 
                                                     bow_doc_x[i][1]))

Word 0 ("order") appears 1 time.
Word 3 ("central") appears 1 time.
Word 4 ("cm") appears 1 time.
Word 5 ("good") appears 1 time.
Word 6 ("government") appears 1 time.
Word 7 ("long") appears 1 time.
Word 8 ("reject") appears 1 time.
Word 9 ("scheme") appears 1 time.

lda_model =  gensim.models.LdaMulticore(bow_corpus, 
                                   num_topics = 10, 
                                   id2word = dictionary,                                    
                                   passes = 10,
                                   workers = 2)

for idx, topic in lda_model.print_topics(-1):
    print("Topic: {} \nWords: {}".format(idx, topic ))
    print("\n")

Topic: 0 
Words: 0.113*"vote" + 0.095*"show" + 0.091*"think" + 0.048*"evm" + 0.048*"leader" + 0.048*"officer" + 0.046*"like" + 0.030*"cm" + 0.025*"home" + 0.025*"today"


Topic: 1 
Words: 0.074*"soldier" + 0.074*"country" + 0.064*"lose" + 0.050*"shah" + 0.043*"pulwama" + 0.043*"compromise" + 0.042*"assembly" + 0.041*"national" + 0.041*"leave" + 0.032*"attack"


Topic: 2 
Words: 0.104*"politics" + 0.046*"corruption" + 0.046*"next" + 0.046*"time" + 0.046*"know" + 0.045*"also" + 0.045*"vote" + 0.044*"see" + 0.032*"govt" + 0.031*"banerjee"


Topic: 3 
Words: 0.071*"want" + 0.070*"govt" + 0.057*"work" + 0.057*"poll" + 0.056*"know" + 0.043*"inr" + 0.043*"political" + 0.043*"workers" + 0.043*"culture" + 0.029*"corruption"


Topic: 4 
Words: 0.069*"nandigram" + 0.069*"years" + 0.061*"mamata" + 0.043*"clear" + 0.043*"lose" + 0.043*"accept" + 0.040*"us" + 0.039*"tell" + 0.035*"challenge" + 0.035*"di"


Topic: 5 
Words: 0.078*"time" + 0.073*"india" + 0.041*"lakh" + 0.041*"major" + 0.041*"also" + 0.041*"commission" + 0.041*"central" + 0.041*"vote" + 0.041*"leader" + 0.041*"elections"


Topic: 6 
Words: 0.121*"nandigram" + 0.067*"banerjee" + 0.064*"mamata" + 0.061*"cm" + 0.050*"political" + 0.050*"compromise" + 0.050*"order" + 0.042*"day" + 0.042*"commission" + 0.026*"like"


Topic: 7 
Words: 0.091*"sonar" + 0.081*"bangla" + 0.080*"youth" + 0.071*"restore" + 0.071*"fulfil" + 0.071*"smile" + 0.071*"farmers" + 0.071*"aspirations" + 0.071*"belief" + 0.071*"poor"


Topic: 8 
Words: 0.132*"hindi" + 0.109*"birthday" + 0.109*"speak" + 0.055*"wave" + 0.046*"call" + 0.046*"good" + 0.045*"like" + 0.045*"workers" + 0.043*"reject" + 0.024*"minister"


Topic: 9 
Words: 0.071*"get" + 0.055*"nd" + 0.052*"elections" + 0.048*"result" + 0.048*"government" + 0.048*"development" + 0.048*"intensify" + 0.048*"formation" + 0.048*"may" + 0.042*"high"

from gensim.models.coherencemodel import CoherenceModel
coherence_model_sc = CoherenceModel(model=lda_model, texts=word_tokken_1, dictionary=dictionary, coherence='c_v')
coherence_lda = coherence_model_sc.get_coherence()
print('\nCoherence Score: ', coherence_lda)

Coherence Score:  0.4004311998140424

The coherence score is for assessing the quality of the learned topics. For one topic, the words i,j being scored in ∑i<jScore(wi,wj) have the highest probability of occurring for that topic.

---

Candidate_analysis_2021 = pd.read_csv("west_bengal.csv")

Candidate_analysis_2021.profile_report()

Candidate_analysis_2021

	candidate	constituency	party	criminal_cases	education	total_assets	liabilities
0	Abdul Hai Mallik	ONDA	IND	0	8th Pass	160171	0
1	Abdur Razzak Molla	FALTA	INC	2	10th Pass	2650450	0
2	Abhijit Bhattacharya	PURULIA	IND	0	Post Graduate	4439532	375000
3	Abir Chandra Mandal	CHHATNA	IND	0	Graduate Professional	809000	0
4	Adhikari Suvendu	NANDIGRAM	BJP	1	Post Graduate	10552749	0
...	...	...	...	...	...	...	...
562	Uttam Naskar	BISHNUPUR (SC)	IND	0	10th Pass	511291	0
563	Uttara Singha Hazra	GARBETA	AITC	1	Graduate	17710285	6348523
564	Vibekananda Mondal	ULUBERIA UTTAR (SC)	IND	0	12th Pass	11953	0
565	Vivekananda Bauri	RAGHUNATHPUR (SC)	BJP	0	Graduate Professional	2030856	0
566	Yamini Kanta Mandi	MANBAZAR (ST)	CPI(M)	0	Post Graduate	3396148	1000000

567 rows × 7 columns

Candidate_analysis_2021["education"].value_counts()

Graduate                 145
12th Pass                106
Post Graduate            105
10th Pass                 94
8th Pass                  54
Graduate Professional     33
Doctorate                  8
Others                     7
5th Pass                   7
Literate                   5
Illiterate                 3
Name: education, dtype: int64

visualization(Candidate_analysis_2021,"education","","Count","Number of Record","Education wise candidate").countplot_with_percentage_representation()

All candidates education history

---

All IND Candidates educational history

IND_candidates_education = Candidate_analysis_2021[(Candidate_analysis_2021["party"]=="IND")]
IND_candidates_education["education"].value_counts()

10th Pass                38
12th Pass                30
Graduate                 21
8th Pass                 20
Post Graduate            16
5th Pass                  4
Illiterate                3
Literate                  2
Graduate Professional     2
Doctorate                 1
Name: education, dtype: int64

visualization(IND_candidates_education,"education","","Count","Number of Record","Education wise candidate").countplot_with_percentage_representation()

visualization(IND_candidates_education,"criminal_cases","","Count","Number of Record","Criminal record of candidate").countplot_with_percentage_representation()

---

AITC_candidates_education = Candidate_analysis_2021[(Candidate_analysis_2021["party"]=="AITC")]
AITC_candidates_education["education"].value_counts()

Graduate                 26
Post Graduate            17
12th Pass                15
Graduate Professional    12
10th Pass                 8
8th Pass                  6
Doctorate                 3
Others                    2
5th Pass                  1
Name: education, dtype: int64

ALL AITC candidate educational history

visualization(AITC_candidates_education,"education","","Count","Number of Record","Education wise candidate").countplot_with_percentage_representation()

ALL AITC candidate criminal history

visualization(AITC_candidates_education,"criminal_cases","","Count","Number of Record","Education wise candidate").countplot_with_percentage_representation()

---

SUCI_candidates_education = Candidate_analysis_2021[(Candidate_analysis_2021["party"]=="SUCI(C)")]
SUCI_candidates_education["education"].value_counts()

Graduate                 21
12th Pass                18
Post Graduate            13
10th Pass                11
8th Pass                  5
Others                    2
Graduate Professional     2
Literate                  1
Doctorate                 1
Name: education, dtype: int64

visualization(SUCI_candidates_education,"education","","Count","Number of Record","Education wise candidate").countplot_with_percentage_representation()

CPI candidates educational background

CPI_candidates_education = Candidate_analysis_2021[(Candidate_analysis_2021["party"]=="CPI")]
CPI_candidates_education["education"].value_counts()

Post Graduate    4
12th Pass        1
Graduate         1
Name: education, dtype: int64

visualization(CPI_candidates_education,"education","","Count","Number of Record","Education wise candidate").countplot_with_percentage_representation()

All BJP candidates educational history

BJP_candidates_education = Candidate_analysis_2021[(Candidate_analysis_2021["party"]=="BJP")]
BJP_candidates_education["education"].value_counts()

Graduate                 27
Post Graduate            20
12th Pass                13
10th Pass                13
8th Pass                  8
Graduate Professional     6
Doctorate                 2
Others                    1
Name: education, dtype: int64

visualization(BJP_candidates_education,"education","","Count","Number of Record","Education wise candidate").countplot_with_percentage_representation()

BJP have highest numbers of graducate and post graducate candidate

---

visualization(BJP_candidates_education,"criminal_cases","","Count","Number of Record","Criminal record wise candidate").countplot_with_percentage_representation()

BJP have highest numbers of criminal candidates

---

Growth rate analysis of West Bengal

Growth_rate_Bengal = web_scrap("https://thewire.in/political-economy/west-bengal-budget-gdp-growth-employment",0).Table_scrap()

Bengal_growth_Dataframe = pd.DataFrame(Growth_rate_Bengal[1:len(Growth_rate_Bengal)+1],columns = Growth_rate_Bengal[0])

Bengal_growth_Dataframe

		GSDP (Constant) Growth % WB Budget 2018-19	GSDP (Constant) Growth % – CSO
0	2012-13	5.6	4.2
1	2013-14	6.5	3.0
2	2014-15	9.0	2.8
3	2015-16	5.9	6.1
4	2016-17	8.0	7.9
5	2017-18	11.5	9.1

Bengal_growth_Dataframe["GSDP (Constant) Growth % WB Budget 2018-19"] = convertor(Bengal_growth_Dataframe["GSDP (Constant) Growth % WB Budget 2018-19"])

Bengal_growth_Dataframe["GSDP (Constant) Growth % – CSO"] = convertor(Bengal_growth_Dataframe["GSDP (Constant) Growth % – CSO"])

Bengal_growth_Dataframe.plot.line()

<AxesSubplot:>

---

Data consistent only records of all the Constituency

Train_data = pd.read_excel("Bengal_train_data.xlsx")

Train_data

	candidate	constituency	party	Winning prediction before election	Final Result
0	ARGHYA ROY PRADHAN (BILU)	Mekliganj	AITC	0	1.0
1	PARESH CHANDRA ADHIKARY	Mekliganj	AIFB	1	0.0
2	DADHIRAM RAY	Mekliganj	BJP	0	0.0
3	JYOTISH ROY	Mekliganj	BSP	0	0.0
4	None of the Above	Mekliganj	NOTA	0	0.0
...	...	...	...	...	...
2250	HAYATUNNINSH BIBI	Murarai	BJP	0	0.0
2251	None of the Above	Murarai	NOTA	0	0.0
2252	BELAL SEKH	Murarai	BSP	0	0.0
2253	MANGAL HEMRAM	Murarai	SUCI	0	0.0
2254	SAMSHUL MIYA	Murarai	RLD	0	0.0

2255 rows × 5 columns

label_Encoder = preprocessing.LabelEncoder()
Train_data["Final Result"]= label_Encoder.fit_transform(Train_data["Final Result"])

class label_enconding:
    def __init__(self,dataframe):
        self.dataframe = dataframe
    def features(self):
        important_columns = ["candidate","constituency","party","Winning prediction before election","Final Result"]
        label_Encoder = preprocessing.LabelEncoder()
        self.dataframe['candidate']= label_Encoder.fit_transform(self.dataframe['candidate'])
        self.dataframe['constituency'] = label_Encoder.fit_transform(self.dataframe['constituency'])
        self.dataframe['party']= label_Encoder.fit_transform(self.dataframe['party'])
        
                
        return self.dataframe      

Data_frame_upd = label_enconding(Train_data).features()

Data_frame_upd

	candidate	constituency	party	Winning prediction before election	Final Result
0	194	194	4	0	1
1	1195	194	3	1	0
2	468	194	9	0	0
3	803	194	11	0	0
4	1168	194	39	0	0
...	...	...	...	...	...
2250	704	201	9	0	0
2251	1168	201	39	0	0
2252	330	201	11	0	0
2253	966	201	52	0	0
2254	1465	201	44	0	0

2255 rows × 5 columns

using different algorithm

X_fold_nn = Data_frame_upd.iloc[:,:-1].values
y_fold_nn = Data_frame_upd.iloc[:,-1].values
kf = KFold(n_splits = 7,random_state = 42,shuffle = True)
for train_index,test_index in kf.split(X_fold_nn):
    X_train_fold_nn,X_test_fold_nn = X_fold_nn[train_index],X_fold_nn[test_index]
    y_train_fold_nn,y_test_fold_nn = y_fold_nn[train_index],y_fold_nn[test_index]

from sklearn.linear_model import LogisticRegression
Classifier_logisticRegression = LogisticRegression(C=0.1,random_state=42)
Classifier_logisticRegression.fit(X_train_fold_nn,y_train_fold_nn)

LogisticRegression(C=0.1, random_state=42)

y_pred = Classifier_logisticRegression.predict(X_test_fold_nn)

from sklearn.metrics import precision_score,recall_score,f1_score,accuracy_score
acc_lr = accuracy_score(y_test_fold_nn,y_pred)
print("accuracy {}".format(acc_lr))

accuracy 0.8975155279503105

---

Random forest classifier

from sklearn.ensemble import RandomForestClassifier
Classifier_kfold_rf =RandomForestClassifier(random_state=42)
Classifier_kfold_rf.fit(X_train_fold_nn,y_train_fold_nn)

RandomForestClassifier(random_state=42)

y_pred_kfold_RF = Classifier_kfold_rf.predict(X_test_fold_nn)

from sklearn.metrics import confusion_matrix,accuracy_score,precision_score,recall_score,f1_score
cm_kfold_RF=confusion_matrix(y_pred_kfold_RF,y_test_fold_nn)
accuracy_kfold_RF = accuracy_score(y_pred_kfold_RF,y_test_fold_nn)
precision_kfold_RF =precision_score(y_pred_kfold_RF,y_test_fold_nn)
recall_kfold_RF = recall_score(y_pred_kfold_RF,y_test_fold_nn)
F1score_kfold_RF =f1_score(y_pred_kfold_RF,y_test_fold_nn)
print(cm_kfold_RF)
print("Accuracy{} and Precission:{} and Recall:{} and F1score:{}".format(accuracy_kfold_RF,precision_kfold_RF,recall_kfold_RF,F1score_kfold_RF))

[[266  13]
 [  9  34]]
Accuracy0.9316770186335404 and Precission:0.723404255319149 and Recall:0.7906976744186046 and F1score:0.7555555555555555

---

Xgboost classifier

from xgboost import XGBClassifier
classifier_xgboost = XGBClassifier()
classifier_xgboost.fit(X_train_fold_nn,y_train_fold_nn)

[18:44:56] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.

XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
              colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,
              importance_type='gain', interaction_constraints='',
              learning_rate=0.300000012, max_delta_step=0, max_depth=6,
              min_child_weight=1, missing=nan, monotone_constraints='()',
              n_estimators=100, n_jobs=12, num_parallel_tree=1,
              objective='multi:softprob', random_state=0, reg_alpha=0,
              reg_lambda=1, scale_pos_weight=None, subsample=1,
              tree_method='exact', validate_parameters=1, verbosity=None)

y_pred_kfold_xgboost = classifier_xgboost.predict(X_test_fold_nn)

from sklearn.metrics import confusion_matrix,accuracy_score,precision_score,recall_score,f1_score
cm_kfold_xg=confusion_matrix(y_pred_kfold_xgboost,y_test_fold_nn)
accuracy_kfold_xg = accuracy_score(y_pred_kfold_xgboost,y_test_fold_nn)
precision_kfold_xg =precision_score(y_pred_kfold_xgboost,y_test_fold_nn)
recall_kfold_xg = recall_score(y_pred_kfold_xgboost,y_test_fold_nn)
F1score_kfold_xg =f1_score(y_pred_kfold_xgboost,y_test_fold_nn)
print(cm_kfold_xg)
print("Accuracy{} and Precission:{} and Recall:{} and F1score:{}".format(accuracy_kfold_xg,precision_kfold_xg,recall_kfold_xg,F1score_kfold_xg))

[[268   8]
 [  7  39]]
Accuracy0.953416149068323 and Precission:0.8297872340425532 and Recall:0.8478260869565217 and F1score:0.8387096774193549

---

Just check the result on test data

Test_data = pd.read_excel("Bengal_test.xlsx")

Test_data_dum = pd.read_excel("Bengal_test.xlsx")

Test_data.head()

	candidate	constituency	party	Winning prediction before election
0	0	4	11	0
1	7	4	4	0
2	9	4	13	0
3	10	4	1	1
4	13	4	8	0

Test_data_cp = Test_data

Data_frame_upd_test = label_enconding(Test_data_cp).features()

y_pred = Classifier_kfold_rf.predict(Data_frame_upd_test.iloc[:,:].values)

y_pred

array([0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 1], dtype=int64)

Test_data_dum["predicted_result"] = list(y_pred)

Test_data_dum.head()

	candidate	constituency	party	Winning prediction before election	predicted_result
0	Abdul Hai Mallik	Onda	IND	0	0
1	Amarnath Shakha	Onda	BJP	0	0
2	Apurba Mondal	Onda	SUCI(C)	0	0
3	Arup Kumar Khan	Onda	AITC	1	1
4	Bikash Patra	Onda	CPI(ML) Red Star	0	0

from sklearn.metrics import roc_curve
from sklearn.metrics import roc_auc_score
# generate a no skill prediction (majority class)
ns_probs = [0 for _ in range(len(y_test_fold_nn))]

rf_probabs = Classifier_kfold_rf.predict_proba(X_test_fold_nn)

rf_probabs =rf_probabs[:,1]

ns_auc = roc_auc_score(y_test_fold_nn, ns_probs)
rf_auc = roc_auc_score(y_test_fold_nn, rf_probabs)

print('No Skill: ROC AUC=%.3f' % (ns_auc))
print('RF: ROC AUC=%.3f' % (rf_auc))

No Skill: ROC AUC=0.500
RF: ROC AUC=0.969

ns_fpr, ns_tpr, _ = roc_curve(y_test_fold_nn, ns_probs)
rf_fpr, rf_tpr, _ = roc_curve(y_test_fold_nn, rf_probabs)

plt.plot(ns_fpr, ns_tpr, linestyle='--', label='No Skill');
plt.plot(rf_fpr, rf_tpr, marker='.', label='Randomforest');
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
# show the legend
plt.legend()

# show the plot
plt.show();

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